bowtie_limma_MA <- knitr::include_graphics("figures/Bowtie_limma_MA_Moore.png")
bowtie_deseq_MA <- knitr::include_graphics("figures/bowtie2_deseq2_maplot.png")
bowtie_edger_MA <- knitr::include_graphics("figures/Bowtie2EdgeR_McGauley.png")
bowtie_limma_MA
bowtie_deseq_MA
bowtie_edger_MA
all_bowtie<- c(bowtie_limma_MA, bowtie_deseq_MA, bowtie_edger_MA)
all_bowtie
## [1] "figures/Bowtie_limma_MA_Moore.png" "figures/bowtie2_deseq2_maplot.png"
## [3] "figures/Bowtie2EdgeR_McGauley.png"
Based on initial viewing of the MA plots where Bowtie2 was the aligner used it looks like the number of differentially expressed genes varies with DE program (limma, DESeq, Edgr). Limma appears to have the least differentually expressed genes. DESeq and Edgr have more DE genes (about the same when comparing the two) but DESeq seems to have genes that are more differentially expressed, eg. further towards 1 and -1 on the y axis.
kallisto_limma_MA<- knitr::include_graphics("figures/Simpson.png")
kallisto_edger_MA<- knitr::include_graphics("figures/MA_kallisto_edgeR.png")
kallisto_deseq_MA<- knitr::include_graphics("figures/kallistoDESeq_Sussman.png")
kallisto_edger_MA
kallisto_limma_MA
kallisto_deseq_MA
all_kallisto_MA<- c(kallisto_edger_MA, kallisto_limma_MA, kallisto_deseq_MA)
Kallisto Limma had one differentially expressed transcript at the p value of 0.01, whereas edgeR and deseq had more differentially expressed transcripts, that seemed comparable to each other. This would support the notion that DE methods make the difference when it comes to differentially expressed transcripts. This is essentially the same thing that the other aligners have mentioned/saw.
salmon_limma_MA <- knitr::include_graphics("figures/McKinleySalmonLimmaMAPlot.png")
salmon_deseq_MA <- knitr::include_graphics("figures/salmon_deseq2_maplot.png")
salmon_edger_MA <- knitr::include_graphics("figures/KE_Salmon_EdgeR_MAPlot.png")
salmon_limma_MA
salmon_deseq_MA
salmon_edger_MA
all_salmon_MA <- c(salmon_limma_MA,salmon_deseq_MA,salmon_edger_MA)
Different aligners identified different numbers of differentially expressed genes, with DESeq finding the least and EdgeR finding the most, though it’s fairly comparable to Limma. DESeq only found 2 significantly up regulated genes and no significantly down regulated genes. All of the genes with this aligner were expressed at lower levels than the others (smaller y axis), though the spread was larger (x axis). Overall, Limma and EdgeR have similar results, but EdgeR identified more genes closer to the x-axis that were differentially expressed compared to Limma, resulting in a curve toward 0.
sailfish_edger_MA <- knitr::include_graphics("figures/JustinKoss.sailfish.edgeR.png")
sailfish_limma_MA <- knitr::include_graphics("figures/Ritter_DEBrowser_image.png")
sailfish_deseq_MA <- knitr::include_graphics("figures/Vogel_Sailfish_DESeq2_MAplot.png")
sailfish_edger_MA
sailfish_limma_MA
sailfish_deseq_MA
It appears that Limma has the least differentially expressed genes out of these three pipelines. EdgeR and DESeq2 have similar amounts of differentially expressed genes in terms of the total number of differentially expressed genes, but DESeq certainly had the most. These two pipelines also seem to have a more normal distribution of differentially expressed genes. There are high and low levels of differential gene expression across different gene abundances. But, in the EdgeR pipeline, it seems that there is a correlation between gene abundance and the the level of differential expression. As the gene becomes more abundant, its level of differential expression decreases. DESeq combines both of these aspects. There is a slight correlation between differential expression and gene abundance at lower abundances, but as abundance increases, the level of differential expression becomes more normally distributed.
bowtie kallisto salmon sailfish
bowtie_limma_MA
kallisto_limma_MA
salmon_limma_MA
sailfish_limma_MA
Of the aligners used with limma, Kallisto had only one differentially expressed gene. Bowtie2 and Sailfish looked very similar though Sailfish seems to have more points closer to 0 on th y axis. Salmon had the most DE genes.
bowtie_deseq_MA
kallisto_deseq_MA
salmon_deseq_MA
sailfish_deseq_MA
For the DESeq aligner salmon only found 2 DE genes, both underexpressed. This is interesting because with limma salmon found the most DE genes. Sailfish seems to have the most genes, but Sailfish, Kallisto, and Bowtie2 seem to all yield similar results. With Kallisto and Sailfish there were several over and under expressed genes beyonf 1 or -1 on the y axis.
bowtie_edger_MA
kallisto_edger_MA
salmon_edger_MA
sailfish_edger_MA
To rank the aligners from most DE genes to least when in combination with EdgeR: Bowtie2 and Salmon with the most, then Sailfish, then Kallisto. However, Sailfish showed the most highly expressed DE genes. Kallisto also has more highly expressed DE genes (closer to 1 and -1). So the two aligners that had the least DE genes, had the most differentially expressed.
Bowtie most: EdgeR? but DESeq has more genes expressed near 1 or -1 Bowtie least: Limma
Kallisto most:DESeq Kallisto least:limma
Salmon most:EdgeR Salmon least:DESeq
Sailfish most: DESeq Sailfish least: Limma
aligner <- c("Bowtie","Kallisto","Salmon","Sailfish")
mostDEaligner <- c("EdgeR","DESeq","EdgeR","DESeq")
leastDEaligner <- c("Limma","Limma","DESeq","Limma")
DE_by_aligner <-data.frame(Aligner=aligner,
Most_DE_Genes=mostDEaligner,
Least_DE_Genes=leastDEaligner)
DE_by_aligner
## Aligner Most_DE_Genes Least_DE_Genes
## 1 Bowtie EdgeR Limma
## 2 Kallisto DESeq Limma
## 3 Salmon EdgeR DESeq
## 4 Sailfish DESeq Limma
Limma most: Salmon Limma least:Kallisto
DESeq most: Sailfish DESeq least:Salmon
EdgeR most:Bowtie or Salmon EdgeR least: Kallisto
program <- c("Limma","DESeq","EdgeR")
mostDEprogram <- c("Salmon","Sailfish","Bowtie/Salmon")
leastDEprogram <- c("Kallisto","Salmon","Kallisto")
DE_by_program <-data.frame(DE_Program=program,
Most_DE_Genes=mostDEprogram,
Least_DE_Genes=leastDEprogram)
DE_by_program
## DE_Program Most_DE_Genes Least_DE_Genes
## 1 Limma Salmon Kallisto
## 2 DESeq Sailfish Salmon
## 3 EdgeR Bowtie/Salmon Kallisto
In conclusion, various pipelines produce varying results. When looking at DE Programs it seems like Kallisto generally finds fewer differentially expressed genes. It looks like Salmon as an aligner was pretty good for 2/3 DE programs. It’s interesting that since Salmon and Kallisto are so similar that a major difference was seen in DE transcripts for Limma (Limma-Salmon: most, Limma-Kallisto: least).
I have not notice a clear trend yet…